Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using categories that you feel may influence fuel efficiency. Answer the following questions.
Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot?
Answer: We choose Avg MPG and Horsepower. We thought the avg mpg and horsepower would have the greatest impact on fuel efficiency.
Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Horsepower is X axis and avg mpg is Y axis.
Do you believe there is a relationship between the two categories? Why or why not?
Answer: Yes - the higher the horsepower the lower the fuel efficiency
If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Answer: Negative slope - as horsepower increases the fuel efficiency decreases
Regression Questions
Create the linear regession equation in Excel. Include both the equation and the r2 value on the graph. Answer the following questions.
What is your regression equation? Explain what the equation means in relation to the categories.
Answer: y=0.067x + 35.44
What is your r2 value? Is this a strong correlation? Why or Why not?
Answer: 0.430
Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not?
Answer: There is some correlation but not a strong correlation between these 2 categories. The closer it is to 1 the stronger the correlation.
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
How would you determine which equation had the best relationship?
Answer: Watch the R2 value - the polynomial was slightly better.
Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Answer: Based on R2 - the polynomial was a better choice.
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.
Back to Activity1Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using categories that you feel may influence fuel efficiency. Answer the following questions.
Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot?
Answer: We choose Avg MPG and Horsepower. We thought the avg mpg and horsepower would have the greatest impact on fuel efficiency.
Answer: Horsepower is X axis and avg mpg is Y axis.Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Yes - the higher the horsepower the lower the fuel efficiencyDo you believe there is a relationship between the two categories? Why or why not?
Answer: Negative slope - as horsepower increases the fuel efficiency decreasesIf there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Regression Questions
Create the linear regession equation in Excel. Include both the equation and the r2 value on the graph. Answer the following questions.
Answer: y=0.067x + 35.44What is your regression equation? Explain what the equation means in relation to the categories.
Answer: 0.430What is your r2 value? Is this a strong correlation? Why or Why not?
Answer: There is some correlation but not a strong correlation between these 2 categories. The closer it is to 1 the stronger the correlation.Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not?
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
Answer: Watch the R2 value - the polynomial was slightly better.How would you determine which equation had the best relationship?
Answer: Based on R2 - the polynomial was a better choice.Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.